Systems and methods concerning tracking models for digital interactions

ABSTRACT

An emotion tracking computer system is presented. An emotion tracking server obtains digital content representing an assertion of the existence of an emotional relationship among multiple entities; e.g., an assertion of gratitude. The server creates one or more instances of emotion objects that model the assertion of the emotional relationship, where such objects are stored in an emotion database. The emotion objects form data primitives that provide opportunities for analyzing contexts of relationships. Gratitude tracking is discussed with some specificity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/275,160, filed Jan. 5, 2016, the contents of which are expresslyincorporated herein by reference.

STATEMENT RE: FEDERALLY SPONSORED RESEARCH/DEVELOPMENT

Not Applicable

TECHNICAL FIELD

The present inventive subject matter relates to computer implementedtechnologies for extracting, categorizing, storing and/or managingdigital models of emotion in digital interactions, especially gratitude.

BACKGROUND

The background description includes information that may be useful inunderstanding the present inventive subject matter. It is not anadmission that any of the information provided herein is prior art orrelevant to the presently claimed inventive subject matter, or that anypublication expressly or implicitly referenced herein is prior art.

Numerous different emotions govern our daily lives. One under-recognizedand under-appreciated human emotion is gratitude. Recent, growingevidence indicates that human lives are positively affected byexpressions of gratitude. Consider the Greater Good Science Center atthe University of California, Berkeley (“GGSC”; see URLgreatergood.berkeley.edu). GGSC has recognized that gratitude is morethan a trivial emotion and that it represents an important aspect of thehuman experience. GGSC also offers access to numerous studies indicatingthe value of expressing gratitude. However, the value of gratituderemains significantly under-recognized to the larger community. In 2012,GGSC launched a web site (called “Thnx4.org”) that provides a platformby which people can share their expressions of gratitude. The platformappears to permit individuals to submit “thanks” as content and journalfeelings of gratitude. GGSC and its website do not appear to modelgratitude in its complexity. Thus, Thanx4.org does not disclose afoundational firmament that models the emotional richness of gratitude.

Others have also put forth efforts to create a web-based platformthrough which individuals can express gratitude. For example, U.S.patent application publication 2012/0254312 to Patel, titled “Methodsand Systems for Incentivizing, Exchanging, and Tracking Expressions ofGratitude within a Network,” filed Mar. 29, 2012, seeks to create aweb-based interface for expressing gratitude via a means called a“thankuconomy” and through which expressions of gratitude areincentivized via a reciprocal gift. The Patel approach does not discloseor emphasize that expressions of gratitude represent an intimateemotional relationship targeting the recipient rather than theinitiator. Further, does not disclose how to create a model forgratitude or other types of emotion-based digital transactions.

From a digital modeling perspective, some minor effort has been directedto creating ontologies for emotion or even for mental functions. As anexample, consider the efforts by the Swiss Centre for Affective Sciencesand University of Buffalo as described by the project found on GitHub atURL github.com/jannahastings/emotion-ontology. The effort describedtherein does not provide any apparent reference to gratitude or insightinto the nature of digital models of gratitude. In the blog post byMichael Uschold titled “Is the Road to Euphoria Paved by Thanking withReckless Abandon?” (see URLsemanticarts.com/blog/ontology-of-gratitude), Uschold describes a simpleview of a small gratitude-based ontology. The Uschold gratitude ontologydoes not disclose how to implement or properly model myriad possiblepoints of gratitude or the full spectrum that gratitude can take.

U.S. patent application publication 2010/0115419 to Mizuno titled“Gratitude Providing System and Gratitude Providing Method,” filed Mar.8, 2007, describes a computer system that allows users to present“chao,” a visual value of information representing the feeling ofappreciation, to another. While recognizing appreciation, Mizuno doesnot identify and model the full spectrum of expressions that gratitudecan encompass.

The known efforts put forth, appear to be directed to informaleffortstoward defining gratitude and do not bring forth sufficient insight intocreating a full model of gratitude. This is further evidenced by thefollowing references that merely make passing reference to gratitude,but do not describe how to create an intimate means of expressing thefull spectrum of gratitude and, in particular, by using technologicalmeans:

-   -   U.S. Pat. No. 5,219,184 to Wolf, titled “Gift Card Incorporating        a Thank You Note and Method,” filed Oct. 27, 1992;    -   U.S. Pat. No. 8,489,527 to von Coppenolle et al., titled “Method        and Apparatus for Neuropsychological Modeling of Human        Experience and Purchasing Behavior,” filed Oct. 21, 2011;    -   U.S. Pat. No. 8,903,751 to Yarbrough et al., titled “Detecting        Participant Helpfulness in Communications,” filed Jan. 28, 2011;    -   U.S. Pat. No. 9,199,122 to Kaleal et al., titled “Personalized        Avatar Responsive to User Physical State and Context”;    -   U.S. patent application publication 2007/0078664 to Grace,        titled “Method and Instrument for Expressing Gratitude for a        Scholastic Experiences,” filed May 19, 2003;    -   U.S. patent application publication 2010/0268580 to Vermes,        titled “Network-Based Simulated Package for Enclosing Digital        Objects,” filed Mar. 22, 2010;    -   U.S. patent application publication 2014/0275740 to Crane,        titled “System for Modifying a User's Neurological Structure or        Neurochemistry by Improving Mood, Positivity Level, or        Resilience Level, Incorporating a Social Networking Website and        Method of Use Thereof” filed Mar. 14, 2014;    -   U.S. patent application publication 2014/0379352 to Gondi et        al., titled “Portable Assistive Device for Combating Autism        Spectrum Disorder,” filed Jun. 16, 2014;    -   U.S. patent application publication 2015/0044654 to Lendvay et        al., titled “Crowed-Sourced Assessment of Technical Skill        (C-SATS™/CSATS™),” filed Aug. 8, 2014;    -   U.S. patent application publication 2015/0140527 to Gilad-Barach        et al., titled “Providing Interventions by Leveraging Popular        Computer Resources,” filed Nov. 19, 2013;    -   U.S. patent application publication 2015/0193718 to Shaburoz et        al. “Emotion Recognition for Workforce Analytics” filed Mar. 23,        2015;    -   U.S. patent application publication 2015/02729832 to Hageman,        titled “Product for Use in the Prophylactic or Therapeutic        Treatment of a Negative Emotion or Introvert Behaviour,” filed        internationally on Jul. 5, 2013; and    -   International patent application publication WO 2014/151999 to        Liu-Qiu-Yan, titled “System and Method to Survey and Evaluative        Items According to People's Perceptions and to Generate        Recommendations Based on People's Perceptions” filed        internationally on Mar. 13, 2014.

Interestingly, the above efforts fail to fully appreciate thatgratitude, and other emotions as well, could have many shades ordimensions associated with it. By understanding, quantifying, andmodeling these shades or dimensions as described in the Applicants' workbelow, gratitude can be made and/or harnessed into a driving force forpositive social impact. Thus, there is a need for computer systems andmethods by which models of emotions, especially models of assertions ofgratitude, can be tracked, managed and analyzed.

All publications identified herein are incorporated by reference to thesame extent as if each individual publication or patent application werespecifically and individually indicated to be incorporated by reference.Where a definition or use of a term in an incorporated reference isinconsistent or contrary to the definition of that term provided herein,the definition of that term provided herein applies and the definitionof that term in the reference does not apply.

In some embodiments, the numbers expressing quantities of ingredientsand/or properties such as concentration, reaction conditions, and soforth, and used to describe and claim certain embodiments of theinventive subject matter are to be understood as being modified in someinstances by the term “about.” Accordingly, in some embodiments, thenumerical parameters set forth in the written description and attachedclaims are approximations that can vary depending upon the desiredproperties sought to be obtained by a particular embodiment. In someembodiments, the numerical parameters should be construed in light ofthe number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theinventive subject matter are approximations, the numerical values setforth in the specific examples are reported as precisely as practicable.The numerical values presented in some embodiments of the inventivesubject matter may contain certain errors necessarily resulting from thestatistical variance calculated from their respective testingmeasurements.

Unless the context dictates the contrary, all ranges set forth hereinshould be interpreted as being inclusive of their endpoints andopen-ended ranges should be interpreted to include only commerciallypractical values. Similarly, all lists of values should be considered asinclusive of intermediate values unless the context indicates thecontrary.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes a plural reference unlessthe context clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise.

The recitation of ranges of values herein is merely intended to serve asa shorthand method of referring individually to each separate valuefalling within the range. Unless otherwise indicated herein, eachpossible individual value is incorporated into the specification as ifit were individually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided with respectto certain embodiments herein is intended merely to better illuminatethe inventive subject matter and does not pose a limitation on the scopeof the inventive subject matter otherwise claimed. No language in thespecification should be construed as indicating any non-claimed elementessential to the practice of the inventive subject matter.

Groupings of alternative elements or embodiments of the inventivesubject matter disclosed herein are not to be construed as limitations.Each group member can be referred to and claimed individually or in anycombination with other members of the group or other elements foundherein. One or more members of a group can be included in, or deletedfrom, a group for reasons of convenience and/or patentability. When anysuch inclusion or deletion occurs, the specification is herein deemed tocontain the group as modified thus fulfilling the written description ofall Markush groups used in the appended claims.

SUMMARY

The inventive subject matter provides apparatuses, systems and methodsin which models of various emotions, especially gratitude, can bedigitally discovered, recognized, recorded, stored, analyzed, orotherwise managed. One aspect of the inventive technology includes acomputer-implemented emotion model tracking system that is able tomanage digital emotion assets and, more specifically, gratitude-basedassets. The tracking system includes at least a computer-based emotiondatabase and an emotion tracking server. The emotion database comprisesa computing device having a tangible, non-transitory computer readablememory (e.g., RAM, SSD, HDD, RAID, NAS, SAN, etc.) storing a number ofindexed emotion objects. Of particular note, each emotion objectdigitally represents an emotional relationship between at least twoentities. For example, a gratitude object can represent an assertion ofgratitude from one person (e.g., a first entity) to another person(e.g., a second entity) for an act of kindness. The emotion objects canbe instances of one or more defined emotion classes having numerous datamembers. More preferred data members of the emotion objects include anemotion object identifier (e.g., a GUID, UUID, name, hash value, etc.)that references the specific emotion object, an initiator identifier(e.g., GUID, name, email address, etc.) that identifies the entity thatinitiates an assertion of the emotional relationship, and a recipientidentifier that identifies the entity that receives or is the target ofthe emotional relationship assertion. An especially interesting datamember of the emotion object includes an emotion metric that representsa value (e.g., a number, scalar, vector, text, multi-valued data, etc.)associated with the asserted emotional relationship; e.g., a measure ofgratitude granted to the recipient.

The emotion tracking server couples with the emotion database to managethe emotion objects. The emotion tracking server is a computing devicehaving at least one processor and also having a tangible, non-transitorycomputer readable memory that stores software instructions. The softwareinstructions, when executed by the processors of the server, cause theserver to take one or more actions related to the emotion objects in theemotion database (e.g., create, move, copy, delete, link, monitor, log,alert, report, secure, etc.). Execution of the instructions causes theserver to receive one or more pieces of digital content from otherdevices (e.g., mobile phones, tablets, web servers, sensors, etc.) wherethe digital content relates or otherwise represents an emotionalrelationship between an initiator entity and a recipient entity; e.g.,an expression of gratitude in the form of a multi-model creativecomposition having images, audio, text, and video. The server, possiblybased on the digital content, obtains a new emotion object identifier toidentify the emotional relationship. The server further derives (e.g.,generates, calculates, looks up, etc.) identifiers for the initiatorentity and the recipient entity that define the emotional relationshipfrom the digital content. Using the obtained or derived emotion objectidentifier and entity identifiers, the server instantiates a new emotionobject according to the emotion class or classes where the new emotionobject specifically models the assertion of the emotional relationshipas represented by the digital content. The server provisions or assignsthe emotion metric of the new emotion object with an instance emotionvalue that can also be derived from the digital content. As examples,the instance emotion value could be a unitary value (i.e., 1), aweighted value, a measured value, a vector, a multi-valued datastructure, or other value that represents the effect or nature of theassertion. The server can further store the new emotion object in theemotion database, possibly by linking it with other associated emotionobjects.

Another, more specific aspect of the inventive subject matter includes acomputer-implemented gratitude model tracking system. The gratitudemodel tracking system is similar to the emotion tracking systemdescribed above and specifically manages gratitude objects. Thegratitude tracking system includes a gratitude database that storesinstances of gratitude objects that model assertions or expressions ofgratitude. The system further includes a gratitude tracking servercoupled with the gratitude database; said server creates instances ofgratitude objects based on received digital content. The instantiatedgratitude objects form a model of the expression of gratitude, includinga gratitude value that can be combined with other gratitude metrics toform a gratitude index or trend. The instantiated gratitude objects arestored in the gratitude database, possibly linked with other gratitudeobjects.

The gratitude model tracking system and method of the inventive subjectmatter enables the tracking of a non-incremental range of expressions ofgratitude or other emotion in a digital exchange, the categorization andstorage of the same, previously unavailable in known systems. Forexample, in some social media settings, a user can either “like” or“dislike” a particular posting, thus providing a positive or negative asit relates to target posting. In other social media settings, forexample, a user might be able to select from an incremental series of“likes”, ranging from 0 to 5. In either case, in known systems anyexpressions toward a target posting or person, would be based on pre-setparameters, without being able to capture a rich range of expressionthat would more accurately model a particular human emotion such asgratitude.

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an overview of an emotion model tracking system.

FIG. 2 illustrates as method of tracking or managing emotion objects.

FIG. 3A and FIG. 3B collectively diagram a data model for storing ormanaging gratitude objects as an example of managing emotion objects.

DETAILED DESCRIPTION

It should be noted that any language directed to a computer should beread to include any suitable combination of computing devices, includingservers, interfaces, systems, databases, agents, peers, engines,controllers, modules, or other types of computing devices operatingindividually or collectively. One should appreciate that the computingdevices comprise a processor configured to execute software instructionsstored on a tangible, non-transitory computer readable storage medium(e.g., hard drive, FPGA, PLA, solid state drive, RAM, flash, ROM, etc.).The software instructions configure or program the computing device toprovide the roles, responsibilities, or other functionality as discussedbelow with respect to the disclosed apparatus. Further, the disclosedtechnologies can be embodied as a computer program product that includesa non-transitory computer readable medium storing the softwareinstructions that cause a processor to execute the disclosed stepsassociated with implementations of computer-based algorithms, processes,methods, or other instructions. In some embodiments, the variousservers, systems, databases, or interfaces exchange data usingstandardized protocols or algorithms, possibly based on HTTP, HTTPS,AES, public-private key exchanges, web service APIs, known financialtransaction protocols, or other electronic information exchangingmethods. Data exchanges among devices can be conducted over apacket-switched network, the Internet, LAN, WAN, VPN, ad-hoc network, apeer-to-peer network, or other type of packet switched network; acircuit switched network; a cell switched network; or another type ofnetwork.

As used in the description herein and throughout the claims that follow,when a system, engine, server, device, module, or other computingelement is described as configured to perform or execute functions ondata in a memory, the meaning of “configured to” or “programmed to” isdefined as one or more processors or cores of the computing elementbeing programmed by a set of software instructions stored in the memoryof the computing element to execute the set of functions on target dataor data objects stored in the memory.

One should appreciate that the disclosed techniques provide manyadvantageous technical benefits including providing computing systemscapable of quickly processing, storing, manipulating, or otherwiseefficiently managing digital objects representing emotionalrelationships. Through leveraging the disclosed technologies, computingdevices can quickly identify or model emotional relationships amongdifferent entities. Such models or data objects can be considereddigital primitives from which additional applications or capabilitiescan be derived.

The focus of the disclosed inventive subject matter is to enable theconstruction or configuration of a computing device to operate on vastquantities of digital data beyond the capabilities of a human, where inthis case the data represents models for myriad emotional relationshipsand especially for those emotional relationships concerning gratitude.Although the digital data represents an emotional relationship, itshould be appreciated that the digital data is a representation of oneor more digital models of the emotional relationship (e.g., an instanceof gratitude), not the emotional relationship itself. By instantiatingsuch digital models in the memory of the computing devices, thecomputing devices are able to manage efficiently the digital data ormodels in a manner that provides to a user of the computing deviceutility that the user would lack without such a tool.

The following discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if such combinations are not explicitly disclosed.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously.

The following subject matter relates to creating digital data objectsrepresenting emotional relationships as emotion objects. Each object canbe considered to represent the relationship as defined by at least twoentities; an initiator and a recipient. In the disclosed approach, theinitiator asserts or otherwise acknowledges that the relationship existswith the recipient. There is no requirement for the recipient toreciprocate or otherwise acknowledge the assertion. In more interestingembodiments, the assertion is based, at least in part, on some item(e.g., previous action, creative work, etc.) associated with therecipient. In view of the expansive nature of the subject matter, thefollowing description presents the subject matter from the perspectiveof gratitude.

Gratitude as an emotion is of special interest due to its unique naturerelative to many other emotions (e.g., love, anger, etc.). Gratituderequires an initial action or existing thing before gratitude can beexpressed. For example, before an initiator can assert gratitude towardanother entity (e.g., person, place, thing, idea, etc.), that entitymust a priori exist in some form (e.g., idea, abstract concept, physicalitem, tangible item, intangible item, etc.). In more typical cases, arecipient entity takes the initial action that gives rise to theinitiator's trigger for asserting gratitude. Thus, gratitude has aquantifiable timeline associated with it and is based on free willactions taken by the recipient and the initiator of the assertion ofgratitude.

It should be understood that there is no requirement that the recipientof gratitude be a person. The recipient of gratitude could be anexisting thing, a tree or forest for example, or an idea, deity, orother item. Therefore, it is possible that the recipient could be anyone of a broad spectrum of items or other inanimate or non-humanentities.

FIG. 1 presents an overview of computer-implemented emotion modeltracking system 100 that tracks or otherwise manages emotion objects 145which represent emotional relationships among or between one or more ofentity 105A and entity 105B, collectively referred to as entities 105.For clarity, the reader is reminded that emotion objects 145 are digitalmodels for emotional relationships where the emotion objects 145 adhereto an overarching model of one or more emotions. The overarching modelcan be considered a “firmament” on which applications can be built. Forexample, a firmament of gratitude can include a defined gratitudeontology, a gratitude namespace, a gratitude grammar, or other formaldescriptions that can embodied digitally to represent gratitude.

Emotion tracking system 100 includes emotion database 140 that storesemotion objects 145 and emotion tracking server 120 that processesemotion objects 145 so that they can be accessed via one or more ofentities 105 over network 115. For the sake of discussion, in theexample shown and from the perspective of gratitude, entity 105Ainitiates an assertion of gratitude toward entity 105B. Although thefollowing description focuses on a single entity 105A assertinggratitude toward a single entity 105B, it should be appreciated thatsuch assertions can include a one-to-many relationship as well as amany-to-many relationship. Further, given the disclosed techniques, itis possible for entity 105A to assert gratitude toward itself. Thus, insome embodiments, entity 105A and entity 105B could be the same entity.

Emotion database 140 comprises one or more computing devices arranged tooperate as a database adhering to a schema, schemata, or other dataorganization for storing emotion objects 145. Emotion database 140 couldinclude a single computer and storage device executing databaseapplication instructions. For example, emotion database 140 couldinclude a server executing an SQL-based relational database applicationthat stores emotion objects 145 on a connected storage sub-system (e.g.,HDD, SSD, SAN, NAS, RAID, etc.). Suitable database applications caninclude Oracle®, PostgreSQL, MySQL, or other types of databaseapplications. Still other types of computing systems are alsocontemplated for emotion database 140.

Larger systems that can be used to implement emotion database 140 caninclude server farms, or even cloud-based systems. Cloud-based databaseapplications could also include SQL-based applications executing onMicrosoft Azure®, Amazon AWS, or Google Cloud SQL. Still, it should beappreciated that a SQL-based database application is not required. Otheroptions could also include schema-less databases such as NoSQL. Still,the reader should fully appreciate that emotion database 140 stores, ina tangible non-transitory computer readable memory, one or more emotionobjects 145 that are indexed for retrieval. In some embodiments, emotionobjects 145 adhere to a defined schema while in other embodiments,emotion objects 145 could be considered as independent tuples of data.

As referenced above, emotion objects 145 digitally model an emotionalrelationship between at least two entities 105. Emotion objects 145adhere to a one or more emotion classes (see FIGS. 3A and 3B) thatinclude several data members aiding in describing the specific nature ofan emotional assertion. In the example shown, entity 105A is assertinggratitude toward entity 105B. Therefore, emotion objects 145 preferablycharacterize such assertions through several data members including anemotion object identifier, an initiator identifier, a recipientidentifier, and at least one emotion metric that quantifies that natureof the asserted emotional relationship.

The emotion object identifier, see new emotion object identifier 131 foran example, is a digital value that identifies a specific instance ofone of emotion objects 145. In some embodiments, the emotion objectidentifier can just be an incremental number. In other embodiments, theemotion object identifier can be a unique emotion object identifier thatuniquely distinguishes an instance of emotion object 145 from all otheremotion objects 145. Example emotion object identifiers can include aGUID, a UUID, a digital object identifier (DOI), a descriptor derivedfrom digital content, an object identifier (OID), an address, a hashvalue, or another type of identifier.

The initiator identifier, see initiator entity identifier 133 as anexample, represents a value that identifies the initiator of theassertion of the emotional relationship. In the example shown, initiatorentity identifier 133 would identify entity 105A. The initiatoridentifier can take on many different forms including an email address,an address (e.g., cryptocurrency address, URL, etc.), a GUID, a name, aunique alias, or other value that identifies the initiator. It should beappreciated that initiator entity 105A identified by the initiatoridentifier could include a wide variety of different types of entities.Although FIG. 1 shows that entity 105A is a human at a desktop computer,initiator entity 105A, in principle, could include a person, an animal,an inanimate object, a place, a thing, a corporation, a product, a workof art, a service, an action, a government, an official, a philosophy, ateam, a religion, an organization, an event, a topic, a deity, amythological item, or other type of entity. While the reader might thinkthat initiator entity 105A would only practically include a human, theapplicants have appreciated that the initiator entity 105A could includeother types of entities as well. For example, a musical artist mightwrite a song about a loved one to assert gratitude toward the loved one.It is true that the artist is asserting gratitude, which would representa first type of assertion. It is also true that the song itself, itslyrics for example, could be considered as putting forth an assertion.Therefore, the applicants have appreciated that gratitude can be teasedapart into many shades or dimensions beyond how gratitude was previouslyconsidered in the past.

The recipient identifier, see recipient entity identifier 135 as anexample, follows a similar form as the initiator identifier and could besimilarly broad in scope. In the example shown, the recipient identifierrepresents entity 105B where entity 105B could also be a person, ananimal, an inanimate object, a place, a thing, a corporation, a product,a work of art, a service, an action, a government, an official, aphilosophy, a team, a religion, an organization, an event, a topic, adeity, a mythological item, or another type of entity. In an embodimentassociated with one or more of social network 107, entity 105A wouldlikely be a person while recipient entity 105B could be just aboutanything, a work of art or an album for example. When the assertion ismade, the assertion can be published over social network 107 (e.g.,Facebook, Instagram, Pinterest, LinkedIn, etc.).

Another point of interest is that the assertion of the emotionalrelationship can be considered identifiable by the at least the pair ofthe initiator identifier and the recipient identifier. Such pairs canthen be used as a foundation for future analytics or analysis.

The emotion metric, see emotion value 137 for an example, is consideredan emotion metric for the asserted emotional relationship. The emotionmetric can be associated with at least one of entities 105 individuallyor collectively. In some embodiments, the emotion metric might be ofunitary value; e.g., an integer of one. In more specific cases, theemotion metric is considered a grant of value from the initiator to therecipient. For example, in the case where entity 105A asserts gratitudetoward entity 105B, a “grät” of 1 is granted to entity 105B asmemorialized in an instantiated version of emotion object 145. It shouldbe appreciated that emotion value 137, as well as the emotion metric,can be single valued (e.g., scalar, text field, etc.) or multi-valued(e.g., vector, matrix, distribution, enumeration, etc.). Additionalconsiderations with regards to emotion metrics are discussed in moredetail below.

Emotion metrics can comprise many different types of values includingparametric values, non-parametric values, text, statistical distributioninformation (e.g., average, mean, mode, width), or other types ofvalues. In some embodiments, the emotion metric can adhere to acustomized enumeration where a stakeholder defines a preferred orderingof values. Consider a situation where a stakeholder wishes to define acustomized enumeration ordering for text values of “high”, “medium”, and“low”. The stakeholder could assign these an order of [“high”: 1,“medium”: 2, “low”: 3] so that high is numerically less than low.Alternatively, the stakeholder could assign a reverse ordering of[“high”: 3, “medium”: 2, “low”: 1] if desired.

Because emotion objects 145 are instances of an emotion class, it ispossible the emotion class can include additional data members beyondthose discussed above, all of which are contemplated herein. Suchadditional data members can be leveraged to track or manage emotionobjects. Example additional data members include a time stamp, a timespan, a location (e.g., room, address, zip code, GPS location, etc.), anentity role type (e.g., manager, employee, etc.), a relationship type(e.g., familial, work, friendship, etc.), or other data members. A veryuseful data member can include one or more emotion object pointers thatpoint to other emotion objects. Such a pointer can include the emotionobject identifier of other emotion objects 145, an address, a URL, oranother type of pointer. Use of such a pointer is advantageous becauseit provides for linking emotion objects 145 together to form chains,possibly including a blockchain or blockchains. Additional pointers caninclude pointers to content (e.g., assertion compositions, videos,images, sounds, etc.), pointers to web content (e.g., addresses, URLs,DOIs, etc.), or other types of pointers. With respect to gratitude, suchchains can be considered a chronicle or ledger of how gratitude isasserted as well as of how gratitude is received, which gives rise tointeresting analytical properties. Thus, emotion tracking server 120 canrespond to requests for information, including how much gratitude hasentity 105A asserted or how much gratitude has entity 105B received andfor what reason. Such information represents inputs and outcomes thatcan be the foundation for training gratitude-based machine learningsystems.

Emotion objects 145 can also include one or more portions of metadataattributes that add additional color to the nature of the assertions.The metadata attributes could take on a wide ranging set of information.Still, in more preferred embodiments, the metadata attributes adhere toa formalized definition so that one of emotion objects 145 can becompared to another. In yet more refined embodiments, the metadataattributes adhere to a normalized, language-independent definition. Forexample, metadata attributes can include name-value pairs where the nameis defined by a meaning identifier (e.g., GUID, hash, UUID, integer,etc.) that corresponds to a normalized meaning while the valuerepresents the specific nature (e.g., text, image, integer value,string, etc.) for the attributes. Consider the example shown from theperspective of gratitude, where entity 105A asserts gratitude towardentity 105B for a past service. The corresponding emotion objects 145would have attributes that include name-value pairs that describe thenature of the past service in a language-independent fashion. A firstattribute might include a name that includes an identifier representinga type of gratitude for “past service” or “nostalgia.” Note that use ofan identifier allows for mapping such an attribute to any language orculture. The corresponding value for the first attribute would include aspecific reference for the past service. The value could include text inthe preferred language of entity 105A, say “Help finding a job” inEnglish for example, or could also include a language independentidentifier that represents the concept of “job assistance” for example.Again, use of language-independent normalized values, although notspecifically required, provides for comparing, contrasting, or analyzingemotion objects 145 no matter their cultural origin.

There are numerous possible types of formal definitions that can be usedto define the attribute space or data space for emotion objects 145.Example types for formal definitions to which the emotion metadataattributes adhere include an emotion ontology, an emotion namespace, anemotion grammar, or other system that models the emotionalrelationships. Each of these types of formalized definitions carrydifferent types of information. In fact, some embodiments use more thanone formalized definition where emotion tracking server 120 can beconfigured to map from one type of definition, say a gratitude ontology,to another type of definition, say a gratitude grammar. The advantage ofsuch an approach is quite important. Each type of representationfacilitates different types of transactions or operations. For example,a gratitude grammar can be considered to be action orientated and wouldthus likely be more useful with binding gratitude-based actions to thirdparties that focus on action (e.g., care organizations, charities,volunteer organizations, etc.). A gratitude ontology can be consideredan overarching description of the nature of gratitude, which can be arepresentation of the firmament through which analysis or analytics canbe performed. A gratitude namespace allows third parties to createcustom representations of gratitude that, for example, can be leveragedfor application-specific purposes. One application specific examplecould include creating a customized and possibly enterprise-specificnamespace for use in an enterprise setting, perhaps to support humanresource management. Still further, each type of formal description orrepresentation for the emotional attribute metadata can adhere to a feeschedule that provides for monetizing the emotional firmament via thirdparties. The third parties could pay or bid to have their brandsassociated with emotional assertions represented by the metadataattributes.

Emotion tracking server 120 is one or more computing devices having atleast one processor and a tangible, non-transitory computer readablememory storing instructions that configures emotion tracking server 120to engage with entities 105, social networks 107, or other third partiesover network 115. Example computing devices include HTTP servers,cloud-based systems (e.g., PaaS, IaaS, SaaS, Gratitude as a Service,etc.), or other types of devices. In some embodiments, emotion trackingserver 120 could include an agent, engine, or module installed on amobile device (e.g., cell phone, tablet, appliance, vehicle, etc.) thatoffers the disclosed roles or responsibilities as capabilities for thedevice. Network 115 can also take on different forms depending on thenature of the deployment of emotion tracking system 100. In more typicalembodiments, network 115 includes the Internet. Still other networks canbe leveraged to facilitate communications among the various devicesincluding cellular networks, intranets, ad hoc networks, peer-to-peernetworks, LANs, WANs, etc.

Emotion tracking server 120 is communicatively coupled with emotiondatabase 140 to facilitate exchange of emotion objects 145. Emotiontracking server 120 and emotion database 140 could be deployed on thesame physical device, say an HTTP server that offers access via a webservice, or could be separate independent devices. For example, emotiontracking server 120 could be embodied as an app deployed on a cell phonewhile emotion database 140 could be a remote server accessed over theInternet or cellular networks.

Emotion tracking server 120 executes the stored instructions on itsprocessor(s) to fulfill its roles or responsibilities associated withtracking or otherwise managing emotion objects 145. Emotion trackingserver 120 receives digital content 123 from at least a first devicewhere the digital content represents the assertion of the emotionalrelationship between at least entity 105A and entity 105B. In thisexample, entity 105A has sent digital content 123 to emotion trackingserver 120 over network 115. Digital content 123 can be considered acomposition representing the assertion and can therefore include one ormore data modalities. Digital content 123 can include text data, imagedata, video data, motion data (e.g., accelerometer data, locations,etc.), audio data, voice data, music data, promotional data, game data,sensor data, healthcare data, biometric data, medical data, enterprisedata, or other types of data. Digital content 123 can be sent using oneor more protocols; HTTP, HTTPS, TCP/IP, etc. Further digital content 123can be serialized or encapsulated within a markup language. For example,an assertion of gratitude, referenced as a “grät” in this document,could be serialized in an XML file having gratitude-based tags definedaccording to one or more of a gratitude ontology, a gratitude namespace,a gratitude namespace, or another formalized definition of gratitude.Digital content 123 can also include additional information, possiblyincluding the address or identifier of the sending entity or destinationentity, time stamps, location information, or other data that can bebound to an instance of an emotion object.

Emotion tracking server 120 attempts to instantiate one or more newemotion objects 125 according to the emotion class from which emotionobjects 145 are built. In order to instantiate new emotion object 125,emotion tracking server 120 provisions the newly created object with oneor more of the necessary emotion objects that have important values. Inthe example shown, emotion tracking server 120 obtains new emotionobject identifier 131, which could include a GUID, UUID, or otheridentifier as previously discussed, and represents the initiatoridentifier for this specific assertion. Such identifiers can beautomatically generated. In some cases, new emotion object identifier131 could be a block identifier within a blockchain, possibly as a hashor address in the Bitcoin blockchain.

Emotion tracking server 120 also derives initiator entity identifier 133from digital content 123. In more simple implementations, initiatorentity identifier 133 could be an email address, a name, a cell phonenumber, or another identifier of entity 105A. Initiator entityidentifier 133 becomes the initiator identifier for the specificassertion represented by new emotion object 125. Similarly, emotiontracking server 120 also derives recipient entity identifier 135 fromdigital content 123. Recipient entity identifier 135 could be explicitlyincluded by name or reference in digital content 123; e.g., an emailaddress or name of entity 105B. However, it is specifically contemplatedthat recipient entity identifier 135 can be derived through one or moreobject recognition techniques including natural language processing,image recognition (e.g., SIFT, DAISY, etc.), audio recognition, or othertypes of recognition processing. For example, if digital content 123includes an image of an album cover, the image can be analyzed viacomputer vision techniques (e.g., OpenCV using SIFT, etc.) to derive oneor more descriptors from the image. The descriptors can then be used tosearch a database configured to return an identifier for the recognizedalbum, assuming the database indexes identifiers based on descriptors.Such a database can be implemented according to search trees and then byleveraging nearest neighbor searches as a function of descriptors. Othertypes of database searches are also contemplated including, for exampleand without limitation, use of keyword searches, direct lookups, filesystem searches. Recipient entity identifier 135 then becomes therecipient identifier for the new emotion object.

The use of the term “derive” with respect to generating identifiers fromthe digital content is also considered to include generating anidentifier de novo. For example, if a recipient is a brand new recipientthat has yet to receive gratitude and has not yet been recorded in adatabase, recipient entity identifier 135 can be generated by emotiontracking server 120 in real-time, possibly through a hash function, viaa GUID API, or by another technique.

Emotion tracking server 120 instantiates new emotion object 125according to the emotion class as a function of new emotion objectidentifier 131, initiator entity identifier 133, and/or recipient entityidentifier 135. It should be appreciated that new emotion objects 125could be instantiated before being provisioned with the data membervalues or could be instantiated along with the values depending on howthe emotion class constructor or methods are called. Although emotionobjects 145 are illustrated as a single data structure, it is alsopossible that emotion objects 145 could be composed of multiple objectsor tables linked together as illustrated in FIGS. 3A and 3B.

Emotion tracking server 120 continues to provision new emotion object125 by assigning an instance emotion value 137 to new emotion object125. The assignment could happen as part of the instantiation process(i.e., the call to the constructor API) or at a later time. For example,the assignment could occur after an optional validation process thatmight be present. Instance emotion value 137 becomes the emotion metricof new emotion object 125.

Once instantiated, emotion tracking server 120 stores new emotion object125 within emotion database 140 as a newly created emotion object 145.Again, it should be noted that new emotion object 125 could beinstantiated and stored without being provisioned, but could beprovisioned by emotion tracking server 120 at a later time.

FIG. 2 illustrates the inventive subject matter from the perspective ofcomputer implemented method 200 for managing one or more emotionobjects. As with the previous discussion, the following description ofmethod 200 will be presented using gratitude as an example where theemotional relationship models an assertion of gratitude. The computingelements of FIG. 1 are considered suitable for implementing method 200.Although method 200 is presented as a linear sequence, it should beappreciated that the order of execution of the steps can include other,differently ordered practical arrangements.

Starting with step 210, an emotion tracking server receives digitalcontent from at least a first device where the digital contentrepresents an assertion or acknowledgement of an emotional relationshipbetween at least two entities. There are several points of noteassociated with this step. The sending device can include a mobiledevice (e.g., cell phone, tablet, gaming system, etc.), an appliance(e.g., a television, etc.), a vehicle (e.g., automobile, etc.), or othercomputing device provisioned with suitable communication interfaces(e.g., 802.11, Bluetooth®, Ethernet, etc.). In more typical scenarios, auser that participates in the ecosystem likely uses an app deployed ontheir cell phone to create a composition of content (i.e., the digitalcontent) to represent the assertion of the emotional relationship.

Consider a scenario where a music lover is enjoying listening to“Blackbird” from the Beatles® White Album®. The music lover wishes toassert gratitude for the existence of the White Album, and possibly byextension of the Beatles themselves. For the sake of this example, thefocus will be on the album itself. The music lover can engage hisgratitude network app on his cell phone to create a composition ofgratitude, i.e., a grät. The composition can include multiple modalitiesof data; perhaps a small audio clip of the song and/or of the musiclover saying “Thank you for bringing beauty into my moment,” an image ofthe music lover smiling, and a video clip of other people swaying to themusic. The app is configured to permit the music lover to arrange thevarious modalities of into the composition. The composition is thencompiled into digital content, which can be sent to the emotion trackingserver. For example, the digital content can be serialized according toone or more formats (e.g., XML, YAML, WSDL, SOAP, etc.) for transmissionover a network via one or more protocols (e.g., HTTP, HTTPS, TCP/IP,UDP/IP, etc.). The digital content can include quite a bit ofinformation beyond the composition representing the assertion ofgratitude including any, some, or all of the music lover's identifier(e.g., email address, phone number, user identifier in the network,etc.), metadata describing the context in which the grät was triggered(e.g., location, time, proximity to others, ambient data, etc.),possibly an identifier for the recipient of the grät, and otherinformation.

Although the previous example focuses on an exchange between a cellphone and an Internet-based gratitude network server, it should beappreciated that the server functionality could also be deployed localto the user. In some embodiments, the emotion tracking serverfunctionality could be deployed as an agent on the user's cell or aspart of an intranet on a closed network.

The application used for the composition of the assertion of gratitudeis best constructed to create, as much as possible, a frictionlessexperience for the user. Thus, the application can provide access tomultiple contextually-relevant pieces of content across multiplemodalities of data at the user's fingertips. This aspect of theapplication is considered to be quite valuable because it reduces thebarrier to creating an intimate assertion of the emotional relationship.For example, when an emotional trigger or sensation which compels theuser to take action is first felt by a user, the application can beconfigured to present one or more pieces of content (e.g., recentlycaptured sound clips, images, ambient data, etc.) that can be used bythe user to create the composition. Further, the application can offerseamless access to the device's sensors (e.g., camera, microphone,biometric sensors, etc.). It is expected that such an approach willallow the user to remain in the moment of intimacy through execution ofsaid user's assertion.

At step 220, the emotion tracking server continues by obtaining a newemotion object identifier. The new emotion object identifier can beobtained from the digital content or it can be locally created. In morepreferred embodiments, each assertion and corresponding emotion objectpassing through the emotion tracking system is given a unique identifier(e.g., GUID, UUID, hash, etc.). The unique identifier can beinstantiated through various techniques, possibly based on one or morepieces of data available to the emotion tracking server: location,initiator identifier, recipient identifier, time, date, metadata, orother type of data values. Ensuring that each emotion object has aunique identifier allows stakeholders to analyze or otherwise manageeach emotion object individually as well as collectively.

At step 230, the emotion tracking server continues with the effort ofcreating an emotion object by deriving from the digital content aninitiator entity identifier for the initiator entity. As discussedabove, the digital content could be provisioned with an initiator'sidentifier. In some embodiments, the initiator entity identifier can bean address (e.g., email address, address within a blockchaininfrastructure, etc.) that preferably uniquely identifies the initiator.

In other embodiments, the emotion tracking server can recognize theinitiator entity from the digital content as suggested by step 235. Theemotion tracking server can be provisioned with one or more recognitionmodules capable of converting various modalities of the digital contentto one or more descriptors or parameters. The descriptors can then beused as a query to a database of indexed initiator identifiers. Considercases where the digital content includes a voice of the initiator and/oran image of the initiator. The recognition module could employ ALIZE(see URL mistral.univ-avignon.fr/mediawiki/index.php/Main_Page) toconvert the digital content into biometrics that can then be used toidentify the initiator. Alternatively, the recognition module could useOpenCV to convert image data into descriptors, which can then be used tolook up the identity of the initiator.

In a similar vein, at step 240, the emotion tracking server derives orotherwise generates from the digital content a recipient entityidentifier for the recipient entity. The reader should keep in mind thatthe nature of the recipient could be quite varied and could include aperson, a thing, a place, a religion, an action, an idea, a mythologicalitem, a deity, or another type of item. Similar to the initiator entityidentifier, the recipient entity identifier is also preferably unique.Still, due to the possible varied nature of the recipient, the recipiententity identifier can be constructed to carry additional information tobetter describe the nature of the recipient. Returning to the example ofa grät granted to the Beatles' White Album, the corresponding recipientidentifier might include a field representing the White Album and afield representing the Beatles. Thus, the resulting recipient entityidentifier could be multi-valued where the fields correspond to anamed-hierarchy or taxonomy. In simpler embodiments, the recipiententity identifier can also be a GUID, UUID, or other unique identifier.In some embodiments, the recipient entity identifier value and theinitiator entity identifier value are assigned according to awell-defined address space; a hash space, network address space, orother address space for example. Assigning identifiers from a commonaddress space provides the advantage of transmitting or routingassertions, or other emotional relationship information, from one entityto another. Naturally, it should be appreciated that it is possiblewithin this disclosed framework for a recipient entity identifier toalso serve as an initiator entity identifier when the circumstancespermit; e.g., a recipient entity asserting gratitude toward anotherentity.

Further in view of the varied nature of the recipient entity andconsidering that the digital content likely carries data about therecipient entity in some form, the emotion tracking server candistinguish the recipient entity from the digital content via theaforementioned recognition module as suggested by step 243. Focusing onjust image data, for example, the emotion tracking server can derive therecipient entity identifier as a function of descriptors calculated fromthe one or more data modalities of the digital content (step 245). As amore specific example and based on embodiments that leverage computervision techniques, consider a case where the digital content includes animage of an actress or other type of artist. The recognition module cancalculate one or more descriptors from the image using recognitionalgorithms; e.g., SIFT, FAST, TILT, DoG, edges. The recognition modulecan submit the descriptors to an object database of known objects wherethe object database could use a nearest neighbor search, or othersuitable lookup technique, to return objects having descriptors that aresimilar and where the known objects are indexed according to previouslycalculated descriptors. The retuned result sets can include informationabout the recognized objects (e.g., artist, actress, etc.), preferablyincluding the recipient entity identifier for the actress or artist.

There will be circumstances where no a priori recipient identifierexists for the recipient entity. In such cases, the emotion trackingserver can generate a brand new recipient entity identifier to representthe recipient entity. The new recipient entity identifier can be createdto adhere to the common address space, if such a space is used, as afunction of the information or data available in the digital content.Consider an example where a college student is the first person toassert gratitude or send a grät to a new “garage band.” The collegestudent takes an image of a poster advertising a recent gig and sendsthe image of the poster through the grät network to a gratitude trackingserver (i.e., an emotion tracking server focused on gratitude). Thegratitude server analyzes the image of the poster via implementationsone or more recognition algorithms and finds no match, at least no matchto within threshold or matching criteria. Finding no match, thegratitude server can generate a new recipient entity identifier for theband, possibly provisioning a corresponding recipient entity object withdata from the image (e.g., a band name, a location, a play list, etc.).The gratitude server can then submit the image of the poster, or theprovisioned recipient entity objects, along with the newly createdrecipient entity identifier, to the known object database for futureuse.

The gratitude server could also return more than one possible recipiententity identifier that satisfies search criteria or other matchcriteria. In such circumstances, the gratitude server can rank therecipient identifiers in the result set according to one or more metrics(e.g., proximity to user, time, user preferences, etc.) and provide backto the initiator the ranked listing of possible recipients. Theinitiator can then select which recipient or recipients are thepreferred target or targets of the grät.

At step 250, once sufficient data is available, the emotion trackingserver instantiates a new emotion object according to an emotion classas a function of the new emotion object identifier, the initiator entityidentifier, and the recipient entity identifier. As discussedpreviously, the emotion class represents a computer definition of a datastructure to model emotional relationships. One possible framework foran emotion class is presented in FIGS. 3A and 3B, which focuses ongratitude. It should be appreciated that the new emotion object could beinstantiated with NULL fields that are then provisioned via one or morecalled methods of the emotion class. Thus, there is no requirement thata call to a constructor include the data values for the identifiers.Alternately, the digital content or portions thereof could be passed tothe constructor of the emotion class, which in turns derives orotherwise obtains the identifiers.

It is also specifically contemplated that instantiation of the newemotion object could be restricted. The reason for such restrictions isto prevent users from “gaming the system” in embodiments where users tryto gain value arising out of their assertions of emotionalrelationships. For example, in embodiments where a gratitude value isbound to an assertion of gratitude and where the gratitude value, orgrät, is given to the recipient, the recipient could continuously grantgräts to itself, thus artificially inflating its own prestige. Step 255references the emotion tracking server restricting of the new emotionobject according to a restriction policy.

Restriction policies aid in preventing users from leveraging the systemtoward their own benefit rather than for the benefit to others. Asalluded to above, in embodiments where emotion objects, say gräts forexample, carry value, restriction polices to prevent undue inflation aidin throttling the creation or the disposition of value. The restrictionpolicies can operate as a function of a wide variety of attributes orother metadata including a time, a time period, the identifier of theinitiator entity or recipient entity, a location, or other metadatavalues. An especially useful restriction policy restricts or preventsinstantiation as a function of both the initiator entity identifier andthe recipient entity identifier. For example, when the emotion trackingserver identifies that the initiator of a gratitude assertion isattempting to make multiple assertions toward the same recipient in ashort time period (e.g., within a minute, an hour, a day, a week, etc.),the gratitude tracking server can prevent duplicative subsequenceassertions. Further, the restriction policy can be configured torestrict insanitation of the new emotional objects when the initiatorentity identifier is somehow linked to the recipient entity identifier.A few examples could include both identifiers being the same (i.e.,restrict assertions of gratitude toward oneself), the identifiersrepresenting a familial connection, the recipient identifier indicatingthe recipient entity is a creative work of the initiator entity, andanother type of link or association among the entities.

In more interesting embodiments, at step 260, the emotion trackingserver also derives an instance emotion value at least in part from thedigital content. As discussed previously, the instance emotion valuerepresents a perceived value of the assertion; a value of an assertionof gratitude for example. In some scenarios, the instant emotion valuecould be set to an integer value; “1,” for example, possibly accordingto the restriction policy. Still, it is specifically contemplated thatthe derivation of the instant emotion value can be more complex. Forexample, if the assertion is first detected by the emotion trackingserver, the corresponding instance emotion value could be set to ahigher value than on subsequent assertions for similar subject matter.Further, the derived instance emotion value could be derived based on acomplexity of the composition representing the assertion. Perhaps anaudio track or play list within the digital content represents asignificant amount of work on the part of the initiator. The amount ofwork associated with the digital content can be quantized (e.g., amountof data, number of data modalities used, reported time spent composingthe digital content, originality of the work, etc.) by the emotiontracking server, and this quantization can then be converted to aninstance emotion value.

An especially interesting scenario for deriving the instant emotionvalue can be found in the case of distributed social networks. Consideran example where a person wishes to assert gratitude for a sports team.The person might compose a grät asserting gratitude for a recent win.The instant gratitude value could be set to “1” so that the teamreceives one unit of gratitude. The grät can then be publicized orpublished through the person's social network, allowing others topiggyback on the grät by asserting their own gratitude. Each subsequentgrät can result in their instance emotion value being weighted accordingto the weight policy (step 263) in view that they were not the trueinitiator of the first assertion of gratitude. For example, the emotiontracking server can weight the instant emotion value as a function ofsocial distance between the initiator entity and subsequent initiators.More specifically, a subsequent initiator might have three degrees ofseparation from the original initiator and see the original grät viaFacebook®. The subsequent initiator piggybacks on the assertion by“re-gräting.” The instance gratitude value of the subsequent initiatorcan be down-weighted, say by the reciprocal of the social distance (⅓),according to the weighting policy. To ensure that a total value does notover-inflate, the weight could, in one exemplary embodiment, be of theform w=b/2^(d), where “w” is the weighted value, “b” is the base value,and “d” is the social distance measured in degrees of separation.

Once the instance emotion value is derived, at step 270, the emotiontracking server assigns the instance emotion value to the net emotionobject's emotion metric, thereby memorializing a value associated withthe assertion of the emotional relationship. It should be appreciatedthat the assignment could occur through the call to a constructor methodor could occur via downstream APIs. In more preferred deployments, theassigned emotion metric will likely remain a static value that can belater retrieved and analyzed as a historical data primitive. However, itis also contemplated that the emotion metric could be dynamic where itchanges with time as new information regarding the assertions becomesavailable. Such an approach gives rise to the ability to generateretrospective gräts once suitable conditions are observed.

At step 280, the emotion tracking server stores the instantiated newemotion object in an emotion database. Storing the new emotion objectcan include making appropriate API calls, SQL for example, to theemotion database. In other simple embodiments, the new emotion objectcan be serialized in a mark-up language or stored in a file where thefile system operates as the emotion object database. In more interestingembodiments, the emotion database could include a distributed ledger;e.g., a blockchain. In such embodiments, emotion objects within theemotion database can be linked to one another (e.g., the new emotionobject is linked to other existing emotion objects at step 185) to formone or more blockchains. An emotion blockchain could be global,personalized, entity-specific, or even external to the disclosedecosystem. For example, the new emotion objects could be stored into anexisting cryptocurrency blockchain (e.g., Bitcoin, Peercoin, Dogecoin,Litecoin, etc.). The advantage to such an approach is that theblockchain forms a verifiable ledger of cross-linked assertions, therebygiving credence to, and verifiable evidence for, any data derived fromthe emotion objects.

Each entity could have one or more individual blockchains. Consideringthat each entity could be an initiator or a recipient, it is possiblethat an entity could have an initiator blockchain having all the emotionobjects that the entity initiated thereby creating a ledger of itsgratitude. The initiator blockchain can be constructed to have onlyemotion objects having a common initiator identifier. Further, the sameentity could also have a recipient blockchain having all emotion objectsthat it has received. Such a recipient-focused blockchain would onlyhave emotion objects having a common recipient identifier. It is alsopossible that each entity just has a single blockchain covering bothsides of the initiator-recipient coin. It is also contemplated that thedisclosed gratitude blockchains could become the foundation for agratitude-based cryptocurrency.

As the emotion database becomes populated with emotion objects and theirassociated emotion metrics, new opportunities present themselves to thevarious stakeholders of the disclosed ecosystem. The emotion objects canbe analyzed or studied to gain insight into the nature of the specificnature of the assertions. For example, the emotion tracking server canderive an emotion index as a function of one or more selected emotionobjects as suggested by step 290. Within the context of gratitude, agratitude tracking server could derive a gratitude ticker representinghow much gratitude has flowed through the system per unit time (e.g.,per minute, per hour, per day, per year, since inception, etc.) bysumming all the gratitude metrics from gratitude objects. Thus, thegratitude index, or other emotion index, can be derived by using acumulative function of emotion metrics of selected emotion objects. Twosimple examples can include deriving the emotion index from emotionmetrics having a common initiator identifier (i.e., how grateful is theinitiator) or from emotion metrics having a common recipient identifier(i.e., how much gratitude is received).

Deriving an emotion index, especially with respect to time, isconsidered advantageous because it allows stakeholders to observetrends, rates of change, higher order derivatives (e.g., d²g/dt²,d³g/dt³, d⁴g/dt⁴, etc.), or other types of metrics. It should beappreciated that the contemplated emotion indices, the gratitude indexfor example, can change with time. In such cases, the emotion trackingserver can update the presentation of a defined emotion index as timepasses, perhaps on a real-time basis similar to a stock ticker.

Beyond using time as a foundation for tracking a gratitude index, otherfactors can also be used. Consider location, for example. A gratitudetracking server can be queried to obtain a results set of gratitudeobjects associated with a specific location; e.g., a zip code, abuilding, a geo-fenced area, a monument, or another location. Thegratitude tracking server can present the gratitude index of thelocation as a function of the emotion metrics of all the gratitudeobjects in the corresponding results set.

Time and location are just two dimensions by which emotion objects couldbe selected. Considering that the each emotion object can be provisionedwith metadata, the number of relevant dimensions by which emotionobjects can be selected for analysis can be quite high. Thus, theemotion index can be derived as a function of emotion objects associatedwith a broad spectrum of selection criteria. For example, the selectioncriteria could depend on metadata including a type of emotion, acategory, a classification, an emotion field, a time, a location, abuilding, a profession, a product, a good, a service, a user definedfield, or another dimension. The contemplated field has measured values(e.g., time, location, etc.) as well as derived values (e.g., headings,trends, etc.). Returning to the example of gratitude, the Applicantshave coined the term “grät field” to mean a gratitude index that can bederived based on a one or more desired dimensions of relevance. The gratfield becomes a valuable resource to third parties because third partiescan bind their actions, brands, goods, or services to the nature of thegratitude, possibly in exchange for a fee. Thus, the inventive subjectmatter is considered to include monetizing the grät field through feeschedules, auctions, or other financial instruments or mechanisms.

Although it is possible to generate negative emotion metrics, theApplicants assert that the use of positive emotion metrics can nudgeusers toward positive behaviors or experiences. Thus, using positiveemotion metrics generates emotion indices that are monotonicallyincreasing. From a gratitude perspective, permitting only positivegratitude values is considered useful for reducing negative impacts onthe user base. As can be appreciated, if other outcomes are desired, orreducing negative impacts on the user base is not a concern, theinventive subject matter of the present application contemplates thatemotions other than gratitude can be utilized.

FIGS. 3A and 3B collectively present an overview of a possible datastructure schema for gratitude objects and an example of how theinventive subject matter associated with emotion objects can beimplemented. The example of FIGS. 3A and 3B was developed using SQL andwill be readily understood by those skilled in the use of SQL. It shouldbe appreciated that the example in FIGS. 3A and 3B is presented forillustrative purposes and should not be considered limiting. Forexample, FIGS. 3A and 3B provides a representation that isrecipient-focused as represented by the grat_object structure.Alternative representations might be initiator foci, or a gratitudeassertion focus (see the grat structure). It is appreciated by theApplicants that the emotion object data structure or table definitionscan take on additional characteristics beyond those shown.

The example gratitude classes or tables comprise numerous featuresconsistent with the previous discussion about emotion classes and giverise to the capabilities of an emotion tracking server as discussedabove. The grat_object represents the high level data constructrepresenting one or more assertions of gratitude toward a recipiententity. Of particular interest, the grat_object includes gratObjectid,which can be considered the recipient identifier discussed previously.In this example, the gratObjectid is represented by an integer having 11digits. However, it is possible for the gratObjectid, as with othermembers of the data structures, to have alternative representations;e.g., 128, 256, or more bits to ensure a sufficient address space tosupport massive numbers of grat_object. Note that the grat structure isthe high level representation of the assertion of gratitude.

In the presented example, emotion metrics within grat_objects arerepresented through several fields. For example, the lifetimeGrats fieldcan be considered a grat index that aggregates the value of allassociated assertions of gratitude toward the person. Further,grat_object_stats also carries a gratitude version of an emotion metricin the field count.

Entity information is represented by the several of the structures,including contact, grat_relationship, and the “user” structures. Forexample, the initiator entity identifier is the gratidFrom field ingrat_relationship and the recipient entity identifier is the gratidTofield in the same structure. Note that the contact structure carriesinformation about the specific entity; e.g., email address, postaladdress, or other information.

There are several other points of interest within the schema outlined inFIGS. 3A and 3B. Note that the grat_object can be represented bygrat_alias. The grat_alias structure includes several fields, includingaliasType and string. These fields allow the identification of agrat_object in any language. For example, one grat_object could includemultiple aliases, perhaps in English using ASCII and/or Japanese inUnicode. This approach is considered advantageous because it providesfor support across languages and for a normalized representation of theunderlying objects; e.g., the grat_object.

Another point of interest is represented by the various metadatastructures, including grat_object_metadata, user_metadata,grat_metadata, groups_metadata, and asset_metadata. These structures arespecifically constructed to support multiple models for representingmetadata, including namespaces, ontologies, grammars, tuples, or otherformalized representations of a gratitude data space. For example, themetadataKey can be an identifier of attribute types in an attributespace. As represented, the metadataKey can represent up to 64 bytesworth of identifiers, a vast space. Further, the metadataString could beused to represent the value of a specific attribute. This approach tometadata allows for objects in the system to accrue nearly any type ofmetadata including global metadata, jurisdictional metadata, culturalmetadata, language metadata, statistics, or other types of metadata. Theasset structure supports pointing to external information possiblyincluding files, images, URLs, APIs, or other external objects.

Yet another point of interest is the use of the group structures. Thegroup structures represent groups of entities rather than a singleentity. Example groups might include friends, workmates, organizations,categories, user-defined collections, or other types of groups.

Again, FIGS. 3A and 3B present one possible implementation of astructure for the creation of gratitude-focused assertions of emotionalrelationships. It is appreciated by the Applicants that the specificrepresentation of FIGS. 3A and 3B would not necessarily be used tocharacterize other types of emotions. Consider happiness, for example.Happiness could be initiated without an a priori action by another orwithout a precursor. Thus, the concept of recipient entity does notnecessarily make sense for happiness. On the other hand, gratituderequires that something that triggers or evokes gratitude within theinitiator exist a priori and that the gratitude be directed to arecipient. Further, the disclosed approach facilitates the positiveexperience associated with gratitude while other emotions can carry anegative experience; e.g., unrequited love.

There are numerous additional considerations associated with theinventive subject matter. The disclosed approach provides a firmamentfor big data analytics. As assertions of gratitude are created, thecontext under which the assertions are made (e.g., sounds, images,locations, etc.) is captured within the metadata. Thus, each assertionevent can be characterized and analyzed individually or collectively.The grät object becomes a data primitive that populates the gratitudedata space. Stakeholders can then engage with the data space todetermine how gratitude flows and attempt to bind their messages orconsumer engagement points based on the gratitude flow.

Given the disclosed infrastructure, it is possible for the assertion ofgräts to be handled on a many-to-many basis. A group of initiators couldband together to operate collectively as an initiator entity. When theinitiation criteria are achieved (e.g., number of participants, timing,etc.), the grät can be sent automatically to the target recipient entityor entities. Additionally, the recipient entity could also includemultiple recipients. When the initiator entity submits a grät to manyrecipients, the server can create individual grät objects automaticallyand send the corresponding gräts to each individual recipient.

Numerous opportunities arise in view that the act of asserting gratitudecan be quantized or characterized via the metadata. As an example,consider the trigger point for assertion of gratitude where a personcomposes an assertion on their cell phone. The person, throughinteractions within their environment, experiences some form of triggerlikely evoked from some object in the environment; e.g., a sound, animage, a video, a remembrance, etc. The person engages with an app ontheir cell phone to convert the evoked feeling into the assertion ofgratitude. At this point, the app can capture data about the user'scontext (e.g., location, time, ambient sounds, etc.), which can bepackaged as metadata for the assertion. Further, the app can query theuser to obtain further clarity about why, or for what reasons, theassertion is being generated or to resolve ambiguities on targetrecipients. This can also be captured as part of the metadata. Assumefor the sake of discussion that the trigger was a feeling of nostalgiawith respect to an image. The metadata could include an attribute of theform <trigger::nostalgia> and recognized features or objects in theimage could also be tagged with this attribute-value pair. Thisattribute in the metadata could then be useful for other third parties;a brand manager for a company could pay, perhaps for exclusivity, tohave their advertising message presented for all assertions based onnostalgia. This small example illustrates how the grat field can becharacterized and quantified for new, novel uses.

A more interesting example of quantifying a gratitude data spaceincludes instantiating new recipient entities representing abstractionsthat are less tangible or that lack an “owning” entity. Consider ascenario where an individual wishes to express gratitude toward the sky.The disclosed techniques provide for instantiating a “sky” recipient ina language-independent, normalized fashion. The newly instantiated skyrecipient can be constructed to collect expressions of gratitude (i.e“gräts”) from others who assert gratitude toward the sky. Thus, suchabstractions are expected to grow in value and would likely become ofgreat interest to third parties as gräts flow to the abstractions. Thirdparties can attach their brands or message to gräts that target suchentities. Additional possible abstractions can include philosophies,deities, vegetation, organisms (e.g., genus, species, etc.), or othertypes of entities that would lack an owner.

In addition to quantizing assertions of gratitude, the aggregation ofinformation across multiple assertions also creates analytic, socialanthropological, novel means of determining the perceived meaning ofbrands, monetization and other opportunities. Just a few examplesinclude creating a year in review where initiators can see how graciousthey have been over the year, recipients can see how much gratitude theyhave received, or other type of review. Still further, the system couldcreate a grät potential, a precursor for the grät field, where usersindicate opportunities for others to take action that would be worthy ofan assertion of gratitude. For example, users could indicategeo-locations in the city that require litter pick-up. Such informationcan be compiled to create a geo-based grät potential field. Others cansee potential “hot spots” and take appropriate action. In response tothe action, they could receive assertions of gratitude from others orthey could receive stored gratitude that is granted upon completion ofaction. Such an approach lays the foundation for gamification ofgratitude to nudge people toward positive behaviors or experiences.

Gräts can be constructed to operate on numerous fronts according toprogrammatic instructions. A grät object can include data members,including instructions that define how the object should behave overtime. Examples include generating continuous gräts that operate as afunction of time (e.g., absolute time, periodic time, relative time,irregular or stochastic time, etc.), generating retrospective grätsbased on detected past patterns in observed metrics (e.g., emotionmetrics, grat data space dimensions of relevance, etc.), or generatingnew gräts based on triggering conditions (e.g., detected trends,observed leading indicators, etc.) just to name a few.

A grät object or series of grät objects could be used to certify ornotarize a social media posting. For example, the system can assemble ablockchain of grät objects. The blockchain of grät objects can includeprivate data along with public data. Upon a particular authorization orpayment, the private data in blockchain of grät objects could beaccessed. The entire blockchain of grät objects could have a hash valuefor all of the data within the block chain, and the private data wouldonly be visible as a hash value, and private data validated byauthorized access upon the chain becoming visible. Third party socialmedia systems and websites (e.g. FaceBook®, Amazon® Reviews) couldaccess the blockchain of grat objects via API or widget. As a personcreates a social media post, the widget or API could obtain a time-stampand hash from the current block in the blockchain of grät objects. Thewidget or API would then create a hash specific for social media postsbased on the time-stamp, the hash of the blockchain of grat objects, andthe content of the social media post. The forgoing data structures havethe advantage of verifying or “notarizing” a social media post via ablockchain of grät objects. Furthermore, the social media post could beconsidered validated to be used for data analysis.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification or claims refer to atleast one of something selected from the group consisting of A, B, C . .. and N, the text should be interpreted as requiring only one elementfrom the group, not A plus N, or B plus N, etc.

What is claimed is:
 1. A computer-implemented emotion model trackingsystem comprising: an emotion database storing in a non-transitorycomputer readable memory a plurality of indexed emotion objects thatdigitally model an emotional relationship between at least two entities,each emotion object adhering to an emotion class and having data membersincluding: an emotion object identifier; an initiator identifier; anrecipient identifier; and an emotion metric representing an emotionvalue for the emotional relationship and associated with at least one ofthe at least two entities; and an emotion tracking servercommunicatively coupled with the emotion database and comprising aprocessor and a non-transitory computer readable memory storinginstructions that when executed by the processor cause the server to:receive digital content from a first device representing an assertion ofan emotional relationship between an initiator entity and a recipiententity; obtain a new emotion object identifier; derive an initiatorentity identifier for the initiator entity from the digital content;derive a recipient entity identifier for the recipient entity from thedigital content; instantiate a new emotion object according to theemotion class as a function of the new emotion object identifier, theinitiator entity identifier, and the recipient entity identifier; assignan instance emotion value, derived at least in part from the digitalcontent, to the emotion metric of the new emotion object; and store thenew emotion object in the emotion database.
 2. The system of claim 1,wherein the emotional relationship models gratitude.
 3. The system ofclaim 1, wherein the emotion object identifier comprises a uniqueemotion object identifier.
 4. The system of claim 3, wherein the uniqueemotion object identifier comprises at least one of the following: aGUID, a UUID, a DOI, a descriptor, and an OID.
 5. The system of claim 1,wherein each emotion object includes one or more of the followingadditional data members: a time stamp, a time span, a location, anentity role type, a relationship type, and a hash.
 6. The system ofclaim 1, wherein each emotion object comprises one or more metadataattributes.
 7. The system of claim 6, wherein the metadata attributescomprise a name-value pair.
 8. The system of claim 6, wherein themetadata attributes adhere to an emotion ontology modeling the emotionalrelationship.
 9. The system of claim 6, wherein the metadata attributesadhere to an emotion namespace modeling the emotional relationship. 10.The system of claim 6, wherein the metadata attributes adhere to anemotion grammar modeling the emotional relationship.
 11. The system ofclaim 6, wherein the metadata attributes adhere to a fee schedule. 12.The system of claim 1, wherein each emotion object includes a pointer toat least one other emotion object.
 13. The system of claim 1, wherein atleast some emotion objects from the plurality of emotion objectscomprise a blockchain.
 14. The system of claim 13, wherein the emotionobjects of the blockchain have a common initiator identifier.
 15. Thesystem of claim 13, wherein the emotion objects of the blockchain have acommon recipient identifier.
 16. The system of claim 1, wherein theinitiator entity includes at one of the following: a person, an animal,an inanimate object, a place, a thing, a corporation, a product, a workof art, a service, an action, a government, an official, a philosophy, ateam, a religion, an organization, an event, a deity, a mythologicalitem, and a topic.
 17. The system of claim 1, wherein the recipiententity includes at one of the following: a person, an animal, aninanimate object, a place, a thing, a corporation, a product, a work ofart, a service, an action, a government, an official, a philosophy, ateam, a religion, an organization, an event, a deity, a mythologicalitem, and a topic.
 18. The system of claim 1, wherein the digitalcontent includes at least one of the following data modalities: textdata, image data, video data, motion data, audio data, voice data, musicdata, promotional data, game data, sensor data, healthcare data,biometric data, medical data, and enterprise data.
 19. The system ofclaim 18, wherein the execution of instructions further causes theserver to derive the recipient entity identifier as a function of adescriptor calculated from the data modalities.
 20. The system of claim1, wherein the instance emotion value comprises a weighted value. 21.The system of claim 20, wherein the weighted value is weighted as afunction of a social distance between the initiator entity and otherprevious initiator entities.
 22. The system of claim 21, wherein thefunction weights the instance emotion value based on the reciprocal ofthe social distance.
 23. The system of claim 1, wherein execution ofinstructions causes the server to restrict instantiation of the newemotion object according to a restriction policy.
 24. The system ofclaim 23, wherein the restriction policy restricts instantiation as afunction of at least one of the following attributes: a time, a timeperiod, the initiator entity identifier, the recipient entityidentifier, a location, and a metadata value.
 25. The system of claim23, wherein the restriction policy restricts instantiation as a functionof both the initiator entity identifier and the recipient entityidentifier.
 26. The system of claim 25, wherein the restriction policyrestricts instantiation based on a criterion that depends on theinitiator identifier being linked to the recipient entity identifier.27. The system of claim 1, wherein execution of instructions furthercauses the server to derive an emotion index as a function of selectedemotion objects from the emotion database.
 28. The system of claim 27,wherein the emotion index comprises a monotonically increasing value.29. The system of claim 27, wherein the emotion index is derived as acumulative function of emotion metrics of the selected emotion objects.30. The system of claim 27, wherein the emotion index is derived as afunction of emotion metrics of the selected emotion objects having acommon initiator identifier.
 31. The system of claim 27, wherein theemotion index is derived as a function of emotion metrics of theselected emotion objects having a common recipient identifier.
 32. Thesystem of claim 27, wherein the emotion index is derived as a functionof emotion metrics of the selected emotion objects associated withselection criteria.
 33. The system of claim 32, wherein the selectioncriteria depend on metadata, including at least one of the following: atype, a category, a classification, an emotion field, a time, alocation, a building, a profession, a product, a good, and a service.34. The system of claim 27, wherein execution of instructions causes theserver to update the emotion index.
 35. The system of claim 1, whereinthe emotion metric is bound to the recipient identifier.
 36. Acomputer-implemented gratitude model tracking system comprising: agratitude database storing in a non-transitory computer readable memorya plurality of indexed gratitude objects that digitally model agratitude relationship between at least two entities, each gratitudeobject adhering to a gratitude class and having data members including:a gratitude object identifier; an initiator identifier; a recipientidentifier; and a gratitude metric representing a gratitude value forthe gratitude relationship and associated with at least one of the atleast two entities; and a gratitude tracking server communicativelycoupled with the gratitude database and comprising a processor and anon-transitory computer readable memory storing instructions that whenexecuted by the processor cause the server to: receive digital contentfrom a first device representing an assertion of a gratituderelationship between an initiator entity and a recipient entity; obtaina new gratitude object identifier; derive an initiator entity identifierfor the initiator entity from the digital content; derive a recipiententity identifier for the recipient entity from the digital content;instantiate a new gratitude object according to the gratitude class as afunction of the new gratitude object identifier, the initiator entityidentifier, and the recipient entity identifier; assign an instancegratitude value, derived at least in part from the digital content, tothe gratitude metric of the new gratitude object; and store the newgratitude object in the gratitude database.